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Sherratt K, Gruson H, Grah R, Johnson H, Niehus R, Prasse B, Sandmann F, Deuschel J, Wolffram D, Abbott S, Ullrich A, Gibson G, Ray EL, Reich NG, Sheldon D, Wang Y, Wattanachit N, Wang L, Trnka J, Obozinski G, Sun T, Thanou D, Pottier L, Krymova E, Meinke JH, Barbarossa MV, Leithäuser N, Mohring J, Schneider J, Włazło J, Fuhrmann J, Lange B, Rodiah I, Baccam P, Gurung H, Stage S, Suchoski B, Budzinski J, Walraven R, Villanueva I, Tucek V, Smid M, Zajíček M, Pérez Álvarez C, Reina B, Bosse NI, Meakin SR, Castro L, Fairchild G, Michaud I, Osthus D, Alaimo Di Loro P, Maruotti A, Eclerová V, Kraus A, Kraus D, Pribylova L, Dimitris B, Li ML, Saksham S, Dehning J, Mohr S, Priesemann V, Redlarski G, Bejar B, Ardenghi G, Parolini N, Ziarelli G, Bock W, Heyder S, Hotz T, Singh DE, Guzman-Merino M, Aznarte JL, Moriña D, Alonso S, Álvarez E, López D, Prats C, Burgard JP, Rodloff A, Zimmermann T, Kuhlmann A, Zibert J, Pennoni F, Divino F, Català M, Lovison G, Giudici P, Tarantino B, Bartolucci F, Jona Lasinio G, Mingione M, Farcomeni A, Srivastava A, Montero-Manso P, Adiga A, Hurt B, Lewis B, Marathe M, Porebski P, Venkatramanan S, Bartczuk RP, Dreger F, Gambin A, Gogolewski K, Gruziel-Słomka M, Krupa B, Moszyński A, Niedzielewski K, Nowosielski J, Radwan M, Rakowski F, Semeniuk M, Szczurek E, Zieliński J, Kisielewski J, Pabjan B, Kirsten H, Kheifetz Y, Scholz M, Biecek P, Bodych M, Filinski M, Idzikowski R, Krueger T, Ozanski T, Bracher J, Funk S. Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. eLife 2023; 12:81916. [PMID: 37083521 DOI: 10.7554/elife.81916] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/20/2023] [Indexed: 04/22/2023] Open
Abstract
Background: Short-term forecasts of infectious disease contribute to situational awareness and capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise forecasts' predictive performance by combining independent models into an ensemble. Here we report the performance of ensemble predictions of COVID-19 cases and deaths across Europe from March 2021 to March 2022. Methods: We created the European COVID-19 Forecast Hub, an online open-access platform where modellers upload weekly forecasts for 32 countries with results publicly visualised and evaluated. We created a weekly ensemble forecast from the equally-weighted average across individual models' predictive quantiles. We measured forecast accuracy using a baseline and relative Weighted Interval Score (rWIS). We retrospectively explored ensemble methods, including weighting by past performance. Results: We collected weekly forecasts from 48 models, of which we evaluated 29 models alongside the ensemble model. The ensemble had a consistently strong performance across countries over time, performing better on rWIS than 91% of forecasts for deaths (N=763 predictions from 20 models), and 83% forecasts for cases (N=886 predictions from 23 models). Performance remained stable over a 4-week horizon for death forecasts but declined with longer horizons for cases. Among ensemble methods, the most influential choice came from using a median average instead of the mean, regardless of weighting component models. Conclusions: Our results support combining independent models into an ensemble forecast to improve epidemiological predictions, and suggest that median averages yield better performance than methods based on means. We highlight that forecast consumers should place more weight on incident death forecasts than case forecasts at horizons greater than two weeks. Funding: European Commission, Ministerio de Ciencia, Innovación y Universidades, FEDER; Agència de Qualitat i Avaluació Sanitàries de Catalunya; Netzwerk Universitätsmedizin; Health Protection Research Unit; Wellcome Trust; European Centre for Disease Prevention and Control; Ministry of Science and Higher Education of Poland; Federal Ministry of Education and Research; Los Alamos National Laboratory; German Free State of Saxony; NCBiR; FISR 2020 Covid-19 I Fase; Spanish Ministry of Health / REACT-UE (FEDER); National Institutes of General Medical Sciences; Ministerio de Sanidad/ISCIII; PERISCOPE European H2020; PERISCOPE European H2021; InPresa; National Institutes of Health, NSF, US Centers for Disease Control and Prevention, Google, University of Virginia, Defense Threat Reduction Agency.
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Affiliation(s)
- Katharine Sherratt
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Hugo Gruson
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Rok Grah
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Helen Johnson
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Bastian Prasse
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | | | | | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Graham Gibson
- University of Massachusetts Amherst, Amherst, United States
| | - Evan L Ray
- University of Massachusetts Amherst, Amherst, United States
| | | | - Daniel Sheldon
- University of Massachusetts Amherst, Amherst, United States
| | - Yijin Wang
- University of Massachusetts Amherst, Amherst, United States
| | | | - Lijing Wang
- Boston Children's Hospital, Boston, United States
| | - Jan Trnka
- Department of Biochemistry, Cell and Molecular Biology, Charles University, Prague, Czech Republic
| | | | - Tao Sun
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Dorina Thanou
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | | | | | | | - Neele Leithäuser
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Jan Mohring
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Johanna Schneider
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | - Jaroslaw Włazło
- Fraunhofer Institute for Industrial Mathematics, Kaiserslautern, Germany
| | | | - Berit Lange
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Isti Rodiah
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | | | | | | | | | | | - Inmaculada Villanueva
- Institut d'Investigacions Biomediques August Pi i Sunyer, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vit Tucek
- Institute of Computer Science, Prague, Czech Republic
| | - Martin Smid
- Institute of Information Theory and Automation, Prague, Czech Republic
| | - Milan Zajíček
- Institute of Information Theory and Automation, Prague, Czech Republic
| | | | | | - Nikos I Bosse
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sophie R Meakin
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Lauren Castro
- Los Alamos National Laboratory, Los Alamos, United States
| | | | - Isaac Michaud
- Los Alamos National Laboratory, Los Alamos, United States
| | - Dave Osthus
- Los Alamos National Laboratory, Los Alamos, United States
| | | | | | | | | | | | | | | | | | - Soni Saksham
- Massachusetts Institute of Technology, Cambridge, United States
| | - Jonas Dehning
- Max-Planck-Institut fur Dynamik und Selbstorganisation, Göttingen, Germany
| | - Sebastian Mohr
- Max-Planck-Institut fur Dynamik und Selbstorganisation, Göttingen, Germany
| | - Viola Priesemann
- MPRG Priesemann, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | | | | | | | | | | | - Wolfgang Bock
- Technical University of Kaiserlautern, Kaiserslautern, Germany
| | | | - Thomas Hotz
- Technische Universitat Ilmenau, Ilmenau, Germany
| | | | | | - Jose L Aznarte
- Universidad Nacional de Educacion a Distancia, Madrid, Spain
| | | | - Sergio Alonso
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Enric Álvarez
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Daniel López
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Clara Prats
- Universitat Politecnica de Catalunya, Barcelona, Spain
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Benjamin Hurt
- University of Virginia, Charlottesville, United States
| | - Bryan Lewis
- University of Virginia, Charlottesville, United States
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Marcin Bodych
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | - Maciej Filinski
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | | | - Tyll Krueger
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | - Tomasz Ozanski
- Wroclaw University of Science and Technology, Wroclaw, Poland
| | | | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Villanueva I, Hauschulz DS, Mejic D, Bryant SJ. Static and dynamic compressive strains influence nitric oxide production and chondrocyte bioactivity when encapsulated in PEG hydrogels of different crosslinking densities. Osteoarthritis Cartilage 2008; 16:909-18. [PMID: 18203631 PMCID: PMC3307988 DOI: 10.1016/j.joca.2007.12.003] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2007] [Accepted: 12/03/2007] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Mechanical loading is an important regulator of chondrocytes; however, many of the mechanisms involved in chondrocyte mechanotransduction still remain unclear. Here, poly(ethylene glycol) (PEG) hydrogels are proposed as a model system to elucidate chondrocyte response due to cell deformation, which is controlled by gel crosslinking (rho(x)). METHODS Bovine articular chondrocytes (50 x 10(6)cells/mL) were encapsulated in gels with three rho(x)s and subjected to static (15% strain) or dynamic (0.3 Hz or 1 Hz, 15% amplitude strain) loading for 48 h. Cell deformation was examined by confocal microscopy. Cell response was assessed by total nitric oxide (NO) production, proteoglycan (PG) synthesis ((35)SO(4)(2-)-incorporation) and cell proliferation (CP) ([(3)H]-thymidine incorporation). Oxygen consumption was assessed using an oxygen biosensor. RESULTS An increase in rho(x) led to lower water contents, higher compressive moduli, and higher cell deformations. Chondrocyte response was dependent on both loading regime and rho(x). For example, under a static strain, NO was not affected, while CP and PG synthesis were inhibited in low rho(x) and stimulated in high rho(x). Dynamic loading resulted in either no effect or an inhibitory effect on NO, CP, and PG synthesis. Overall, our results showed correlations between NO and CP and/or PG synthesis under static and dynamic (0.3 Hz) loading. This finding was attributed to the hypoxic environment that resulted from the high cell-seeding density. CONCLUSION This study demonstrates gel rho(x) and loading condition influence NO, CP, and PG synthesis. Under a hypoxic environment and certain loading conditions, NO appears to have a positive effect on chondrocyte bioactivity.
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Affiliation(s)
- I Villanueva
- Department of Chemical Engineering, University of Colorado, Campus Box 424, Engineering Center, ECCH 111, Boulder, CO 80309-0424, USA
| | - DS Hauschulz
- Department of Chemical Engineering, University of Colorado, Campus Box 424, Engineering Center, ECCH 111, Boulder, CO 80309-0424, USA
| | - D Mejic
- Department of Chemical Engineering, University of Colorado, Campus Box 424, Engineering Center, ECCH 111, Boulder, CO 80309-0424, USA
| | - SJ Bryant
- Department of Chemical Engineering, University of Colorado, Campus Box 424, Engineering Center, ECCH 111, Boulder, CO 80309-0424, USA,Corresponding author: Department of Chemical and Biological Engineering, UCB 424 ECCH 111, Boulder, CO, 80309, USA. Tel: (303) 735-6714; Fax: (303) 492-4341;
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Abstract
BACKGROUND Adalimumab is a fully human anti-TNFalpha monoclonal antibody. Published studies indicate that its use in patients with RA can be effective and safe. OBJECTIVES The aim of this review was to assess the efficacy and safety of adalimumab in the treatment of RA. SEARCH STRATEGY Electronic databases were searched up to August, 2004: MEDLINE, CINAHL, EBM Reviews (CDSR, ACP Journal Club, DARE and CENTRAL) and Health STAR. Conference proceedings were hand searched and pharmaceutical companies were contacted to obtain additional unpublished data from published trials. Adalimumab was searched as a text word as it is not currently indexed. The search was not limited by language, year of publication or type of publication. SELECTION CRITERIA All randomised controlled trials (RCTs) or controlled clinical trials (CCTs) comparing adalimumab alone or in combination with DMARDs to placebo or other DMARDs. DATA COLLECTION AND ANALYSIS Two reviewers independently collected the data in a standardized form and assessed the methodological quality of the trial using validated criteria. Outcome measures included ACR and EULAR responses, DAS 28 and components of ACR response and radiographic data. Safety data were also included. Continuous data were reported as weighted mean difference (WMD) with 95% confidence interval (95%CI), absolute benefit (AB) and relative difference (RD). Dichotomous outcomes were reported as relative risk (RR) with 95% CI, absolute risk difference (ARD) or risk difference (RDiff) with 95%CI and number needed to treat (NNT) or to harm (NNH). When significant heterogeneity was not found, data were pooled. MAIN RESULTS Six studies with 2381 patients were included in this review. Two comparisons were done: A. adalimumab subcutaneously (sc) + methotrexate (or DMARDs) versus placebo sc + methotrexate (or DMARDs). B. adalimumab sc in monotherapy versus placebo sc. In the comparison A, with adalimumab 40 mg every other week (e.o.w.), the RR to achieve an ACR 20 response at 24 weeks ranged in the included studies from 1.52 to 4.63, and the NNT ranged from 1.9 to 5.4. The RR (95%CI) to achieve an ACR 50 response was 4.63 (3.04-7.05), and the NNT was 3.0 (95%CI 2.0-6.0). The RR (95%CI) to achieve an ACR 70 response was 5.14 (3.14-8.41) and the number needed to treat was 7.0 (95%CI 5.0-13.0). At 52 weeks, the RRs (95%CI) to achieve an ACR 20, 50, and 70 response were 2.46 (1.87-3.22), 4.37 (2.77-6.91), and 5.15 (2.60-10.22), with NNTs of 2.9, 3.1, and 5.3, respectively. At 52 weeks, adalimumab 40 mg e.o.w. and 20 mg every week (e.w.) significantly slowed the radiological progression including Sharp modified index, erosion score, and joint space score (only with 40 mg e.o.w.). In the comparison B, with adalimumab 40 mg e.o w. , the RRs to achieve an ACR 20, 50, and 70 response at 24/26 weeks were 1.91 (1.17-3.10), 2.84 (1.58-5.12), and 7.33 (2.25-33.90) with NNTs of 5.0 (95%CI 3.0-9.0), 7.0 (4.0-20.0), and 9.0 (3.0-38.0), respectively. In most of the analysed studies and comparisons, there were not significant differences in safety outcomes between adalimumab and control groups. The development of positive antinuclear antibodies was significantly more frequent in adalimumab patients than in placebo patients. Serious infections were significantly more frequent in adalimumab patients in only one study (Keystone 2004) with a RR (95%CI) of 7.64(1.02-57.18) and a NNH of 30.2. AUTHORS' CONCLUSIONS On the basis of the studies reviewed here, adalimumab in combination with methotrexate is efficacious and safe in the treatment of the rheumatoid arthritis. Adalimumab 40 mg sc e.o.w. and 20 mg e.w. slows the radiographic progression at 52 weeks. Adalimumab in combination with DMARDs other than methotrexate is also efficacious and safe, even though data from one only study are available and the number of patients in each group is low. Adalimumab in monotherapy is efficacious and safe in the treatment of the rheumatoid arthritis but the effect size is lower than with combined therapy.
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Affiliation(s)
- F Navarro-Sarabia
- Rheumatology, Hospital Universitario Virgen Macarena, Dr Fedriani, 3, Seville, Spain, 41009.
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